Robust Parallel Covariance Intersection Fusion Predictor for AR Signal Systems With Uncertain Noise Variances and Missing Measurements

A robust parallel covariance intersection (PCI) fusion predictor for autoregressive (AR) systems facing mixed uncertainties is introduced. These mixed uncertainties comprise uncertain noise variances and missing measurements, which are prevalent and pose significant challenges in practical applications. The design methodology of the PCI fusion predictor is composed of three key steps. First, there is model conversion. By leveraging the state‐space signal predictor and the fictitious approach, the original AR system is transformed into a multimodel system. Next comes the design of both local and PCI fusion predictors. Based on the minimax robust estimation principle and the PCI fusion algorithm, these predictors are developed. Finally, the robustness and robust accuracies of these predictors are verified. The matrix conversion method is employed to prove their robustness. To validate the proposed results, a simulation example is carried out. The simulation results clearly demonstrate the correctness and effectiveness of the developed predictors.